Intelligent emergency stop algorithm for a manipulator using a new regression method

Area of Science:

• Robotics and Automation
• Artificial Intelligence
• Safety Engineering

Background:

• Accidental collisions involving large manipulators pose significant risks, leading to personal injuries and equipment damage.
• Existing emergency stop systems often lack the sophistication to handle complex scenarios, necessitating advanced solutions.

Purpose of the Study:

• To develop and evaluate an intelligent emergency stop system for manipulators.
• To enhance operational safety and efficiency by preventing accidental collisions.
• To introduce an algorithm that considers manipulator motion and object classification.

Main Methods:

• Development of an intelligent emergency stop algorithm incorporating manipulator motion direction.
• Implementation of a novel regression method for distinguishing collision-causing obstacles from manipulator parts.
• Application and performance assessment of the system on a two-link manipulator.

Main Results:

• The proposed intelligent algorithm demonstrated effective identification of collision risks.
• The system successfully differentiated between genuine obstacles and manipulator components.
• Performance evaluation showed the efficacy of the intelligent algorithm compared to existing models.

Conclusions:

• The intelligent emergency stop system offers a promising solution for improving manipulator safety.
• The algorithm's ability to consider motion and classify objects enhances its reliability in preventing collisions.
• This approach contributes to safer human-robot interaction in industrial environments.